Image-to-Image Translation Method for Game-Character Face Generation

Citations

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초록

Traditional image-to-image translation methods effectively change the style; however, these methods have several limitations in shape changing. Particularly, current image-to-image translation technology is not effective for changing a real-world face image to the face of a virtual character. To solve this problem, we propose a novel unsupervised image-to-image translation method that is specialized in facial changes accompanied by radical shape changes. We apply two feature loss functions specialized for faces in an image-to-image translation technique based on the generative adversarial network framework. The experimental results show that the proposed method is superior to other recent image-to-image algorithms in case of face deformations.

키워드

Image-to-image Translation; Generative Adversarial Network; Game-Character Generation
제목
Image-to-Image Translation Method for Game-Character Face Generation
저자
Kang, Shinjin; Ok, Yoonchan; Kim, Hwanhee; Hahn, Teasung
발행일
2020
유형
Proceedings Paper
저널명
2020 IEEE CONFERENCE ON GAMES (IEEE COG 2020)
페이지
628 ~ 631